Intelligent acceptance check for towers of overhead transmission line based on point clouds
The acceptance check is a key step to decide whether an overhead transmission line can be put into use. However, for the acceptance check of towers, the manual means is still adopted mostly nowadays, which is dangerous and inefficient. The main challenges for intelligent check technique are the auto...
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| Published in | IET generation, transmission & distribution Vol. 17; no. 22; pp. 5074 - 5089 |
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| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Wiley
01.11.2023
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| Subjects | |
| Online Access | Get full text |
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| Abstract | The acceptance check is a key step to decide whether an overhead transmission line can be put into use. However, for the acceptance check of towers, the manual means is still adopted mostly nowadays, which is dangerous and inefficient. The main challenges for intelligent check technique are the automatic tower segmentation and intelligent requirements of some detection items. Here, based on point clouds from lidars, some intelligent methods are proposed for tower related acceptance check items. The geometric structural changing rules for towers are found and analyzed to identify and segment towers from the scene point cloud automatically. A model‐constrained method is proposed for fine filtering of towers. With point cloud registration strategies, the tilt angle of a tower is calculated intelligently and the defect detection of a tower is conducted successfully. Experiments show that the results of the proposed methods are accurate, efficient and automatic, which have potential for real applications. |
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| AbstractList | Abstract The acceptance check is a key step to decide whether an overhead transmission line can be put into use. However, for the acceptance check of towers, the manual means is still adopted mostly nowadays, which is dangerous and inefficient. The main challenges for intelligent check technique are the automatic tower segmentation and intelligent requirements of some detection items. Here, based on point clouds from lidars, some intelligent methods are proposed for tower related acceptance check items. The geometric structural changing rules for towers are found and analyzed to identify and segment towers from the scene point cloud automatically. A model‐constrained method is proposed for fine filtering of towers. With point cloud registration strategies, the tilt angle of a tower is calculated intelligently and the defect detection of a tower is conducted successfully. Experiments show that the results of the proposed methods are accurate, efficient and automatic, which have potential for real applications. The acceptance check is a key step to decide whether an overhead transmission line can be put into use. However, for the acceptance check of towers, the manual means is still adopted mostly nowadays, which is dangerous and inefficient. The main challenges for intelligent check technique are the automatic tower segmentation and intelligent requirements of some detection items. Here, based on point clouds from lidars, some intelligent methods are proposed for tower related acceptance check items. The geometric structural changing rules for towers are found and analyzed to identify and segment towers from the scene point cloud automatically. A model‐constrained method is proposed for fine filtering of towers. With point cloud registration strategies, the tilt angle of a tower is calculated intelligently and the defect detection of a tower is conducted successfully. Experiments show that the results of the proposed methods are accurate, efficient and automatic, which have potential for real applications. |
| Author | Ma, Miao Du, Songlin Zhang, Kanjian Chen, Yuxin Li, Junyang Li, Yuhong Liu, Huan Wei, Haikun Jiang, Xuancheng Xia, Siyu Wang, Chenxing |
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| References | Yang W. (e_1_2_9_13_1) 2018; 27 e_1_2_9_30_1 e_1_2_9_31_1 e_1_2_9_11_1 e_1_2_9_34_1 e_1_2_9_10_1 e_1_2_9_35_1 e_1_2_9_32_1 Shen Y. (e_1_2_9_7_1) 2023; 118 Wang W. (e_1_2_9_3_1) 2018; 8 e_1_2_9_15_1 e_1_2_9_38_1 e_1_2_9_14_1 e_1_2_9_39_1 e_1_2_9_17_1 e_1_2_9_36_1 e_1_2_9_16_1 Guo T. (e_1_2_9_5_1) 2018; 33 Zhao X. (e_1_2_9_12_1) 2018; 18 e_1_2_9_19_1 e_1_2_9_18_1 e_1_2_9_20_1 e_1_2_9_22_1 e_1_2_9_21_1 e_1_2_9_24_1 e_1_2_9_8_1 e_1_2_9_6_1 e_1_2_9_4_1 Shen X. (e_1_2_9_33_1) 2017; 31 e_1_2_9_2_1 Che K. (e_1_2_9_23_1) 2019; 51 Sohn G. (e_1_2_9_28_1) 2012; 13 e_1_2_9_26_1 Rodrigues O. (e_1_2_9_37_1) 1840; 5 e_1_2_9_25_1 e_1_2_9_27_1 e_1_2_9_29_1 Peng X. (e_1_2_9_9_1) 2017; 41 |
| References_xml | – ident: e_1_2_9_31_1 doi: 10.3390/rs15092371 – volume: 13 start-page: 28 year: 2012 ident: e_1_2_9_28_1 article-title: Automatic powerline scene classification and reconstruction using airborne lidar data publication-title: ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. – ident: e_1_2_9_22_1 doi: 10.1016/j.isprsjprs.2020.03.018 – ident: e_1_2_9_25_1 doi: 10.1109/ICUAS.2014.6842267 – ident: e_1_2_9_27_1 doi: 10.1007/s42835-019-00230-w – volume: 41 start-page: 3670 year: 2017 ident: e_1_2_9_9_1 article-title: Research on automatic positioning algorithm of power transmission towers based on UAV LiDAR publication-title: Power Syst. Technol. – ident: e_1_2_9_16_1 doi: 10.1109/PandaFPE57779.2023.10140294 – volume: 27 start-page: 30 year: 2018 ident: e_1_2_9_13_1 article-title: Research on high‐voltage line tower slope and curvature deformation based on TLS publication-title: Eng. Surv. Mapp. – ident: e_1_2_9_8_1 doi: 10.1109/TIM.2023.3293543 – ident: e_1_2_9_36_1 – ident: e_1_2_9_39_1 – ident: e_1_2_9_30_1 doi: 10.1109/JSTARS.2021.3133602 – volume: 18 start-page: 257 year: 2018 ident: e_1_2_9_12_1 article-title: A detection method for transmission line tower deformations based on binding bars publication-title: Sci. Tech. Energy – ident: e_1_2_9_18_1 doi: 10.3390/app13031688 – ident: e_1_2_9_10_1 doi: 10.3390/rs11222600 – ident: e_1_2_9_15_1 doi: 10.1088/1742-6596/2030/1/012076 – ident: e_1_2_9_20_1 doi: 10.3390/rs10040613 – ident: e_1_2_9_2_1 – ident: e_1_2_9_24_1 – volume: 31 start-page: 516 year: 2017 ident: e_1_2_9_33_1 article-title: Inclination measurement of transmission line tower based on terrestrial 3D lidar publication-title: J. Electron. Measur. Instrum. – ident: e_1_2_9_35_1 doi: 10.1109/MRA.2012.2206675 – ident: e_1_2_9_38_1 – volume: 33 start-page: 530 year: 2018 ident: e_1_2_9_5_1 article-title: Study on power tower extraction and fast positioning from airborne LiDAR data publication-title: Remote Sens. Technol. Appl. – ident: e_1_2_9_21_1 doi: 10.1109/TGRS.2020.2989470 – volume: 51 start-page: 121 year: 2019 ident: e_1_2_9_23_1 article-title: Inspection of electric transmission line fault based on UAV lidar publication-title: Nonlinear Opt. Quantum Opt. – ident: e_1_2_9_11_1 doi: 10.3390/rs11151798 – ident: e_1_2_9_19_1 doi: 10.3390/app13010407 – ident: e_1_2_9_6_1 doi: 10.1109/ICARSC52212.2021.9429772 – ident: e_1_2_9_4_1 doi: 10.5194/isprs-annals-IV-4-W4-81-2017 – volume: 5 start-page: 380 year: 1840 ident: e_1_2_9_37_1 article-title: Des lois géométriques qui régissent les déplacements d'un système solide dans l'espace, et de la variation des coordonnées provenant de ses déplacements consideérés indépendamment des causes qui peuvent les produire publication-title: J. Math. Pures Appl. – volume: 118 year: 2023 ident: e_1_2_9_7_1 article-title: An automatic framework for pylon detection by a hierarchical coarse‐to‐fine segmentation of powerline corridors from UAV LiDAR point clouds publication-title: Int. J. Appl. Earth Obs. Geoinf. – ident: e_1_2_9_29_1 doi: 10.5194/isprs-annals-IV-2-W4-165-2017 – ident: e_1_2_9_32_1 doi: 10.1016/j.ijepes.2021.106987 – ident: e_1_2_9_34_1 – ident: e_1_2_9_17_1 doi: 10.3390/rs14020408 – ident: e_1_2_9_26_1 doi: 10.23919/ChiCC.2019.8866322 – volume: 8 start-page: 10 year: 2018 ident: e_1_2_9_3_1 article-title: Comparison of inclination measurement methods of power tower publication-title: Electron. Power Surv. Design – ident: e_1_2_9_14_1 doi: 10.1109/EI252483.2021.9713663 |
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| SubjectTerms | computer vision power system security power transmission lines |
| Title | Intelligent acceptance check for towers of overhead transmission line based on point clouds |
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